261 research outputs found
Structured Prediction Problem Archive
Structured prediction problems are one of the fundamental tools in machinelearning. In order to facilitate algorithm development for their numericalsolution, we collect in one place a large number of datasets in easy to readformats for a diverse set of problem classes. We provide archival links todatasets, description of the considered problems and problem formats, and ashort summary of problem characteristics including size, number of instancesetc. For reference we also give a non-exhaustive selection of algorithmsproposed in the literature for their solution. We hope that this centralrepository will make benchmarking and comparison to established works easier.We welcome submission of interesting new datasets and algorithms for inclusionin our archive.<br
Tensorizing Neural Networks
Deep neural networks currently demonstrate state-of-the-art performance in
several domains. At the same time, models of this class are very demanding in
terms of computational resources. In particular, a large amount of memory is
required by commonly used fully-connected layers, making it hard to use the
models on low-end devices and stopping the further increase of the model size.
In this paper we convert the dense weight matrices of the fully-connected
layers to the Tensor Train format such that the number of parameters is reduced
by a huge factor and at the same time the expressive power of the layer is
preserved. In particular, for the Very Deep VGG networks we report the
compression factor of the dense weight matrix of a fully-connected layer up to
200000 times leading to the compression factor of the whole network up to 7
times
Learning Markov Random Fields for Combinatorial Structures via Sampling through Lov\'asz Local Lemma
Learning to generate complex combinatorial structures satisfying constraints
will have transformative impacts in many application domains. However, it is
beyond the capabilities of existing approaches due to the highly intractable
nature of the embedded probabilistic inference. Prior works spend most of the
training time learning to separate valid from invalid structures but do not
learn the inductive biases of valid structures. We develop NEural Lov\'asz
Sampler (Nelson), which embeds the sampler through Lov\'asz Local Lemma (LLL)
as a fully differentiable neural network layer. Our Nelson-CD embeds this
sampler into the contrastive divergence learning process of Markov random
fields. Nelson allows us to obtain valid samples from the current model
distribution. Contrastive divergence is then applied to separate these samples
from those in the training set. Nelson is implemented as a fully differentiable
neural net, taking advantage of the parallelism of GPUs. Experimental results
on several real-world domains reveal that Nelson learns to generate 100\% valid
structures, while baselines either time out or cannot ensure validity. Nelson
also outperforms other approaches in running time, log-likelihood, and MAP
scores.Comment: accepted by AAAI 2023. The first two authors contribute equall
Structured Prediction Problem Archive
Structured prediction problems are one of the fundamental tools in machine
learning. In order to facilitate algorithm development for their numerical
solution, we collect in one place a large number of datasets in easy to read
formats for a diverse set of problem classes. We provide archival links to
datasets, description of the considered problems and problem formats, and a
short summary of problem characteristics including size, number of instances
etc. For reference we also give a non-exhaustive selection of algorithms
proposed in the literature for their solution. We hope that this central
repository will make benchmarking and comparison to established works easier.
We welcome submission of interesting new datasets and algorithms for inclusion
in our archive.Comment: Added multicast instances from Andres grou
Higher-order Graph Principles towards Non-rigid Surface Registration
This report casts surface registration as the problem of finding a set of discrete correspondences through the minimization of an energy function, which is composed of geometric and appearance matching costs, as well as higher-order deformation priors. Two higher-order graph-based formulations are proposed under different deformation assumptions. The first formulation encodes isometric deformations using conformal geometry in a higher-order graph matching problem, which is solved through dual-decomposition and is able to handle partial matching. Despite the isometry assumption, this approach is able to robustly match sparse feature point sets on surfaces undergoing highly anisometric deformations. Nevertheless, its performance degrades significantly when addressing anisometric registration for a set of densely sampled points. This issue is rigorously addressed subsequently through a novel deformation model that is able to handle arbitrary diffeomorphisms between two surfaces. Such a deformation model is introduced into a higher-order Markov Random Field for dense surface registration, and is inferred using a new parallel and memory efficient algorithm. To deal with the prohibitive search space, we design an efficient way to select a number of matching candidates for each point of the source surface based on the matching results of a sparse set of points. A series of experiments demonstrate the accuracy and the efficiency of the proposed framework, notably in challenging cases of large and/or anisometric deformations, or surfaces that are partially occluded.Ce rapport formalise le problème du recalage de surfaces 3D comme la recherche d’un ensemble de correspondances discrètes par la minimisation d’une fonction d’énergie, qui est composée de fonctions de coûts mesurant des similitudes géométriques et d’apparence, et des à priori d’ordre élevé sur la déformation. Deux formulations à base de graphes d’ordre élevé sont proposées sous différentes hypothèses de déformation. La première formulation encode la déformation isométrique, à partir de géométrie conforme, dans un problème d’appariement de graphes d’ordre élevé, qui est résolu par décomposition duale et est capable de gérer les cas de correspondance partielle. Malgré l’hypothèse d’isométrie, cette approche est capable de mettre en correspondance de manière robuste deux ensembles clairsemés de points sur deux surfaces, y compris lorsque celles-ci subissent une déformation fortement anisométrique. Cependant, sa performance se dégrade de manière significative lorsqu’elle est étendue au recalage anisométrique d’un ensemble de points à forte densité. Ce problème est rigoureusement traité par la suite à travers un nouveau modèle de déformation capable de gérer des difféomorphismes arbitraires entre deux surfaces. Ce modèle de déformation est introduit dans une formulation MRF d’ordre élevé pour le recalage dense de surfaces, et être inféré en utilisant un nouvel algorithme parallèle et efficace en termes de mémoire. Pour traiter l’espace de recherche prohibitif, nous concevons une méthode efficace pour sélectionner un ensemble de correspondants potentiels pour chaque point appartenant à la surface source. Cette méthode est basée sur les résultats d’appariement d’un ensemble clairsemé de points. Notre méthode est validée au moyen d’une série d’expériences qui démontrent sa précision et son efficacité, notamment dans les cas difficiles où des déformations importantes et/ou anisométriques sont présentes, ou lorsque les maillages sont partiellement cachés
- …